The acquisition and tracking of targets in an image sequence presents a difficult and challenging image processing problem. To date, numerous approaches for automatic target tracking have been proposed. Many of these algorithms suffer from excessive constraints imposed on the motion of the targets being tracked and the need for an explicit motion model. Such constraints may include requirements for strong intensity boundaries or constraints on the target shape, e.g., the target acquisition may require the target to belong to a parameterized family of shapes.
Motion detection and target tracking can be of critical importance in conducting submarine operations, such as surface operations in high contact density environments, where collision avoidance is critical. Difficulties in detection and tracking can be acerbated by poor situational awareness brought about by the lack of a truly integrated approach in assimilating acoustic and non-acoustic data. Combat control operators may be unable to rapidly process multiple surface contacts while operating at periscope depth, which can compromise own ship safety and jeopardize mission success. The solution to these problems may require improved sensor information management and system automation at several levels.
Present submarine surface operations can require manual extraction and analysis of periscope video data, including numerous separate actions required to process a single periscope report. This processing can result in long time delays in situational assessments, which can increase own ship vulnerability to collision. Thus, the inability to rapidly extract and process tactical information from periscope observations in high contact density environments can compromise ship, and more importantly, crew safety.
Typically, when conducting operations at periscope depth, tactical information on visual contacts can be manually entered into the combat system for further analysis. Although contact bearing can be automatically extracted based on periscope data, it may be manually input into the combat system. Contact range and orientation may be both manually extracted and manually input. Automatic correlation with sonar contacts can be typically performed using simple bearing or bearing-range gating functions. While the traditional approach may be adequate for low contact density, blue water scenarios, for high contact density scenarios, as may be common in littoral environments, this approach can be too operator intensive, especially in poor visibility and close contact spacing. Shallow water operations can constrain own ship depth excursions while high contact density scenarios can constrain own ship course and speed maneuvers. These limitations can require faster reaction times for situational assessment and avoidance of collision.
In order to reduce risks when operating in such environments, successful approaches to target detection and tracking can include addressing boundary constraints in order to operate under visually impaired conditions, e.g., low-light, poor weather, etc. Additionally, the shape constraints can be addressed to avoid a plethora of false detections from common littoral features such as landmasses, bridges and other man-made structures. Further, in high-density contact management (HDCM) problems, a successful algorithm can include automatic handling of target occlusions that can occur when targets pass in front of each other.